Community regeneration has drawn much attention in both the urban development and sustainable design fields in the last decade. As a response to the regeneration challenges of Shanghai’s old and high-density communities, this article proposes two design-driven strategies: enabling residents to become innovation protagonists and facilitating collaborative entrepreneurial clusters based on the reorganization of community resources. Two ongoing collaborative projects between the Siping community and Tongji University—Open Your Space microregeneration (OYS) and the Neighborhood of Innovation, Creativity, and Entrepreneurship Towards 2035 (NICE 2035) living labs project—are adopted as main case studies. Research findings are put forward through a structured analysis of qualitative data. Firstly, we reviewed the situation and sustainable goals for Shanghai’s old residential communities, and how design-centric social innovation and collaboration can be effective interventions. Secondly, we analyzed resident empowerment approaches to decision-making, co-design, and co-management processes in OYS with participatory observation. Finally, through participants’ interviews and key events analysis in NICE 2035, we investigated how living labs reuse community distributed resources to develop lifestyle-based business prototypes. The inquiry of this article proposes a co-creation mechanism and action guides towards localized and sustainable community regeneration, which can provide a contextual paradigm for similar challenges.

A stroke is the basic limb movement that both humans and animals naturally and repetitiously perform. Having been introduced into gestural interaction, mid-air stroke gestures saw a wide application range and quite intuitive use. In this paper, we present an approach for building command-to-gesture mapping that exploits the semantic association between interactive commands and the directions of mid-air unistroke gestures. Directional unistroke gestures make use of the symmetry of the semantics of commands, which makes a more systematic gesture set for users’ cognition and reduces the number of gestures users need to learn. However, the learnability of the directional unistroke gestures is varying with different commands. Through a user elicitation study, a gesture set containing eight directional mid-air unistroke gestures was selected by subjective ratings of the direction in respect to its association degree with the corresponding command. We evaluated this gesture set in a following study to investigate the learnability issue, and the directional mid-air unistroke gestures and user-preferred freehand gestures were compared. Our findings can offer preliminary evidence that “return”, “save”, “turn-off” and “mute” are the interaction commands more applicable to using directional mid-air unistrokes, which may have implication for the design of mid-air gestures in human–computer interaction.

The process of visual analytics is composed of the visual data exploration tasks supporting analytical reasoning. When performing analytical tasks with the interactive visual interfaces displayed by the large screen, physical discomforts such as gorilla-arm effect can be easily caused. To enrich the input space for analysts, there has been some researches concerning the cross-device analysis combining mobile devices with the large display. Although the effectiveness of expert-level designs has been demonstrated, little is known of the ordinary users’ preferences for using a mobile device to issue commands, especially the small one like smartwatch. We implement a three-stage study to investigate and validate these preferences. A total of 181 distinctive gestural inputs and 52 interface designs for 21 tasks were collected from analysts. Expert designers selected the best practices from these user-defined interactions. A performance test was subsequently developed to assess the selected interactions in terms of quantitative statistics and subjective ratings. Our work provides empirical support and proposes a set of design guidelines for optimizing watch-based interactions aimed at remote control of visual data on the large display. Through this research, we hope to advance the development of smartwatches as visual analytics tools and provide visual analysts with a better usage experience.